Computational analysis for infectious diseases surveillance and host-pathogen interactions (in the context of Influenza A viruses)

Author

Beyene, Biruhalem Taye

Date of Issue

2017

School

School of Biological Sciences

Related Organization

Bioinformatics Institute (BII) Agency for Science, Technology and Research (A*STAR)

Abstract

Influenza A virus (IAV) is a major public health problem responsible for the death of half
a million people every year worldwide. Zoonotic transmissions of the virus from swine and avian
origin have occurred and can in the worst-case result in pandemics. Prediction of the next
pandemic strain is still a major challenge as mechanisms of antigenic shift and the zoonotic
nature of influenza viruses are still poorly understood. On the other hand, the current vaccination
strategy treating seasonal influenza viruses, given its own problems in efficacy, is not well suited
to mitigate an impending pandemic. While there are few clinically approved anti-IAV drugs for
therapeutic and prophylactic use, these drugs have also been challenged by the emergence of
drug resistance, toxicity, adverse effects and low efficacy. Hence, development of a broad range
anti-IAV drug that could target all of the IAV strains irrespective of their source is critically
important.
To discover effective IAV vaccines and alternative anti-IAV therapeutics, it is crucial to
understand how IAVs adapt to different species’ host ranges and vice versa, how different
species hosts respond to different IAV subtypes. Additionally, it is crucial to understand IAVhost interactions and identifying critical viral or host factors that could support the replication of
the virus or induce pathologic conditions. Recently, high throughput technologies such as mRNA
microarray-based gene expression, genome-wide siRNA screening, and proteomic analysis are
providing in-depth insights into host-pathogen interactions of IAVs. Hence, this study aimed to
computationally investigate IAV-host interactions using three data set (transcriptome, genomewide siRNA screens and interactome) and identify 1) virus and host specific responses, 2) host
determinants in host adaptations and 3) host targets for IAV therapeutics.
This thesis contains four main projects. In the first project (chapter 3), we investigated the
host gene expression changes of eight IAVs (H1N1/WSN, pH1N1, H5N2/F118, H5N2/F59,
H5N2/F189, H5N3, H7N9 and H9N2 viruses) in A549 cells at different time points of infections.
Then we integrated the differentially expressed genes (DEGs) in at least 3 viruses, with 1713 and
1780 host factors comprehensively curated from 11 siRNA and 13 interactome studies
respectively. The integration of the three data set highlighted plausible influenza A virus required
host factors (IHFs) that could be targeted against IAVs. The up-regulated IHFs (e.g. TRIM21,
TRIM26, IRF2, and SAMHD1) might support the replication of IAV through suppression of the innate and adaptive antiviral immune responses. The other up-regulated IHFs could also enhance
the replication of IAV at different stages of the virus lifecycle: endocytosis (BTC), prevention of
apoptosis (TIMM17A), nuclear import (JAK2), and translation elongation of viral proteins
(HEXIM1). Although several of these IHFs have been implicated in other viruses, the detailed
mechanisms of how several of these up-regulated IHFs could support IAV replication require
further investigation.
The second project (chapter 4), explored a comprehensive analysis of host gene
expression changes in different IAV infections in different host species. We used host gene
expression signatures of cell lines from three species (A549 (human), CEF (chicken), and
MDCK (canine) in response to six IAVs (H1N1/WSN, H5N2/F59, H5N2/F118, H5N2/F189,
H5N3 and H9N2 viruses). To compare the expression changes between the three species, we
performed comprehensive probe set re-annotation and human ortholog mapping. The result
showed that the expression signatures of different IAV isolates in a single species cell type are
more similar to each other compared to the expression signatures of a single isolate in different
species’ cell types. The functional annotations (pathways) and the highly expressed cell-specific
signatures indicated that IAVs up-regulated host factors could induce virus infectivity (e.g.
OSBPL1A and ARHGAP21), reduce apoptosis (e.g. MRPS27) and increase cell proliferation (e.g.
COPS2) in CEF cells. Conversely, increased antiviral, pro-apoptotic and inflammatory
signatures have been identified in A549 cells. Except in H5N3 virus infections, generally IAV
infections down-regulates genes associated with cell cycle and metabolic pathways with the
strongest effect in MDCK cells, followed by A549 cells in a strain dependent manner, but not in
CEF cells. Previously our group demonstrated that the replication of the examined viruses was
significantly higher in CEF cells than the other cell types. Thus, we hypothesise that this could
partially explain the mechanism how infectious LPAI viruses shed by chickens lack the
inflammatory response and cellular disruption that may lead to disease conditions.
In the third project (chapter 5), we used a systems-based approach to investigate changes
to the transcriptome of primary murine lung macrophages (PMФ) in response to infection with
the mouse-adapted H1N1/WSN virus and low pathogenic avian influenza (LPAI) viruses H5N2
and H5N3. The results showed that while all viruses induced antiviral responses, the H5N3
infection resulted in higher expression levels of cytokines and chemokines associated with
inflammatory responses. Previously, our group showed that the LPAI H5N2 and H5N3 were able to infect murine lung macrophages and together with increased expression of inflammatory
mediators particularly in H5N3 virus could impose threats to human health in the future.
The IHFs identified by the IAV genome-wide siRNA screening studies could be used as
potential anti-IV targets. However, these studies were not consistent (reproducible) mainly due to
false negative results. Hence, in the fourth project (chapter 6), we applied computational gene
network growing for discovering gene network links that could have been missed by these
experimental investigations. Using the known IHFs we compared the network growing function
of two free tools GeneMANIA and STRING and the commercial IPA for their performance of
recovering other known IHFs previously identified from siRNA screens. The result showed that,
given small (~30 genes) or medium (~150 genes) input sets, all three network growing tools
detected significantly more known host factors than random human genes with STRING overall
performing strongest. Extending the networks with all the three tools significantly improved the
detection of GO biological processes of known host factors compared to not growing networks.
Notably, the rate of identification of true host factors using computational network growing is
equal or better to doing another experimental siRNA screening study which could also be true
and applied to other biological pathways/processes.
Finally, using the known IHFs and the "new IHF candidates" (genes connected to the
IHFs from the network growing analysis), we predicted computational drug-target interactions
using MetaCore. We identified 343 US Food and Drug Administration (FDA) approved drugs
that had an inhibitory effect on either the known or new candidate IHFs, of which 258 were new
predictions. Furthermore, using different criteria, we computationally ranked the 343 FDA
approved drugs for further experimental validation.